Building an Enterprise Chatbot : Work with Protected Enterprise Data Using Open Source Frameworks

Explore the adoption of chatbots in business by focusing on the design, deployment, and continuous improvement of chatbots in a business, with a single use-case from the banking and insurance sector. This book starts by identifying the business processes in the banking and insurance industry. This i...

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Main Authors: Singh, Abhishek, Ramasubramanian, Karthik (Author), Shivam, Shrey (Author)
Corporate Author: SpringerLink (Online service)
Format: eBook
Language:English
Published: Berkeley, CA Apress 2019, 2019
Edition:1st ed. 2019
Subjects:
Online Access:
Collection: Springer eBooks 2005- - Collection details see MPG.ReNa
Summary:Explore the adoption of chatbots in business by focusing on the design, deployment, and continuous improvement of chatbots in a business, with a single use-case from the banking and insurance sector. This book starts by identifying the business processes in the banking and insurance industry. This involves data collection from sources such as conversations from customer service centers, online chats, emails, and other NLP sources. You’ll then design the solution architecture of the chatbot. Once the architecture is framed, the author goes on to explain natural language understanding (NLU), natural language processing (NLP), and natural language generation (NLG) with examples. In the next section, you’ll discuss the importance of data transfers using natural language platforms, such as Dialogflow and LUIS, and see why this is a key process for chatbot development. In the final section, you’ll work with the RASA and Botpress frameworks. By the end of Building an Enterprise Chatbot with Python, you will be able to design and develop an enterprise-ready conversational chatbot using an open source development platform to serve the end user. You will: Identify business processes Design the solution architecture for a chatbot Integrate chatbots with internal data sources using APIs Discover the differences between natural language understanding (NLU), natural language processing (NLP), and natural language generation (NLG) Work with deployment and continuous improvement through representational learning
Physical Description:XXII, 385 p. 102 illus online resource
ISBN:9781484250341